标题:Course Similarity Calculation Using Efficient Manifold Ranking
作者:Zhao, Bingjie; Li, Xueqing
通讯作者:Li, Xueqing
作者机构:[Zhao, Bingjie; Li, Xueqing] Shandong Univ, Sch Comp Sci & Technol, Jinan 250100, Peoples R China.
会议名称:8th International Conference on Knowledge Science, Engineering and Management (KSEM)
会议日期:OCT 28-30, 2015
来源:KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, KSEM 2015
出版年:2015
卷:9403
页码:421-432
DOI:10.1007/978-3-319-25159-2_38
关键词:Course similarity calculation; Text mining; EMR; Major similarity; calculation
摘要:Course Similarity Calculation aims at quantitatively computing the cross degree of the knowledge points two courses contain. However, the polysemy and synonym of various knowledge points lead to the main challenge for calculation effectiveness. Existing course similarity calculation methods are mainly based on the traditional text mining approaches such as Latent Semantic Indexing (LSI) and Term Frequency-Inverse Document Frequency (TFIDF). However, these methods calculate the similarity between two courses simply by their absolute pairwise distance, which significantly limits the effectiveness of capturing the semantic relevance among all the courses. In this paper, we propose a novel course similarity calculation method using Efficient Manifold Ranking (EMR), which improves the traditional methods by measuring course similarities considering the underlying intrinsic manifold structure on the whole dataset. Experimental results on a real world course database demonstrate the outstanding performance of our proposed method. Furthermore, we extend the proposed method to major similarity calculation.
收录类别:CPCI-S;EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84951843463&doi=10.1007%2f978-3-319-25159-2_38&partnerID=40&md5=e90a6a5fa9bbb057f304461a5cc99d12
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